Fusion of digital information is used in areas such as battlefield monitoring and control operations. A battlefield may be observed by a large number of various types of sensors, each of which more-or-less continuously monitors its “field of view” to produce digital information which is evidence of the nature or characteristics of the object or objects within the field of view. If the platform on which the sensor is mounted is moving, the field of view may change with time. Such sensors receive raw data from an observed direction or space in either a passive or active manner, and process the information according to some algorithm in order to make a determination of the nature of the object or condition. For example, a radar system operating as an active sensor may transmit radar signals in a desired direction, and then processes signals returned from a target to determine various characteristics of the received signal in order to characterize the target as, say, an F-15 fighter aircraft rather than a C-130 transport aircraft. A passive sensor might detect the emissions of an active sensor carried by a remote platform, determine the type of sensor detected, and identify platforms capable of carrying such an active sensor. Another passive sensor might respond to the spectral frequency distribution of a jet engine. In all cases, the raw sensed signals are processed to put them into the form of evidence of a characteristic of the object, and the evidence is applied to a decision-making algorithm to produce taxonomic (type) evidence as to the nature of the object.
A great deal of work has been done in the field of fusion of the outputs of various sensors associated with a battle region, in order to combine or rationalize the results of the many sensors observing the region from different vantage points under different operating conditions. Work has also been done on the combining or rationalization of the data produced by each individual sensor during sequential observations of its field.
Bayes equation has been used to update likelihood estimates from new sensor data or measurements. In FIG. 1, a system 10 includes a sensor 12 which observes a region designated generally as 18 lying between skewed field-of-view lines 18′. Sensor 12 includes active or passive transducers and their associated electronics, illustrated as a block 14. Block 14, if active, may be, for example, a radar or lidar system, which transmits signals into region 18 and receives return or reflected signals from targets therein, as for example target object 20. In the case of a passive sensor, block 14 may be, for example, a sensor which senses emissions radiated by a jet engine. Of whatever type, transducer block 14 produces “raw” signals which can be processed to determine some characteristics of the object 20.
The raw transducer data produced by block 14 of FIG. 1 is applied to a processing block 16 and processed to extract information about the object. Such processing, in the case of a radar or lidar, might determine the range of the target from the transducer, its altitude and speed, and possibly some information about its dimensions. In the case of a passive sensor, the emitted radiations might be applied to a spectrum analyzer to determine the frequency distribution. The processed data produced by block 16 represents evidence of the characteristics of the target or object. The evidence information or data produced by block 16 is applied to a block 22.
The evidence data or information applied to block 22 is further processed to produce a taxonomic (type) classification of the sensed object 20. Such a classification in the case of a radar type sensor might use knowledge of the speed of a target in conjunction with its size to determine that it is a fighter aircraft rather than a cargo aircraft. An emission sensor might deem an object having a dominant emission frequency of 100 KHz to be an aircraft with a J-100 engine, and an object with a dominant emission frequency of 120 KHz to include a J-150 engine. The evidence information is subject to error, and the results are described in terms of probabilities. In one prior art arrangement, block 22 performs the taxonomic classification by the use of Bayes equation or algorithm. Use of Bayes equation to update likelihood estimates typically assumes a uniform distribution for initialization, then uses the previous estimate as a prior distribution. Bayes conditional equations can be formed relating objects and evidence of their characteristics
                              P          ⁡                      (                          a              |                              E                1                                      )                          =                                            p              ⁡                              (                                                      E                    1                                    |                  a                                )                                      ⁢                          p              ⁡                              (                a                )                                                                                        p                ⁡                                  (                                                            E                      1                                        |                    a                                    )                                            ⁢                              p                ⁡                                  (                  a                  )                                                      +                                          p                ⁡                                  (                                                            E                      1                                        |                    b                                    )                                            ⁢                              p                ⁡                                  (                  b                  )                                                                                1      where
a and b are types of possible objects that might be observed;
p(a) is the prior probability that the object is type a;
p(b) is the prior probability that the object is type b;
P(a|E1) is the probability that the object is type a when the sensor produced evidence E1.
P(E1|a) is the probability that the sensor produces evidence E1 when type a is observed.
P(E1|b) is the probability that the sensor produces evidence E1 when type b is observed. In equation 1, the possible densities of objects, which is to say the prior probabilities p(a) and p(b) are in truth binary and complementary, so that p(a) is either 1 or 0. However, there is no way to know which type is observed, and no means, other than multiple observations as used in the prior art, to select or identify one of type a and type b over the other. The resulting taxonomic determination is made available at a sensor output port 12o. 
Improved or alternative fusion is desired.